Subject: Coupling function
Subject: Cross-frequency coupling
Subject: Bayesian inference
Subject: EEG
Subject: sleep
Year: 2023
Type: Article
Title: Neural Cross-Frequency Coupling Functions in Sleep
Author: Dragana Manasova
Author: Tomislav Stankovski
Abstract: The human brain presents a heavily connected complex system. From a relatively fixed anatomy, it can enable a vast repertoire of functions. One important brain function is the process of natural sleep, which alters consciousness and voluntary muscle activity. On neural level, these alterations are accompanied by changes of the brain connectivity. In order to reveal the changes of connectivity associated with sleep, we present a methodological framework for reconstruction and assessment of functional interaction mechanisms. By analyzing EEG (electroencephalogram) recordings from human whole night sleep, first, we applied a time-frequency wavelet transform to study the existence and strength of brainwave oscillations. Then we applied a dynamical Bayesian inference on the phase dynamics in the presence of noise. With this method we reconstructed the cross-frequency coupling functions, which revealed the mechanism of how the interactions occur and manifest. We focus our analysis on the delta-alpha coupling function and observe how this cross-frequency coupling changes during the different sleep stages. The results demonstrated that the delta-alpha coupling function was increasing gradually from Awake to NREM3 (non-rapid eye movement), but only during NREM2 and NREM3 deep sleep it was significant in respect of surrogate data testing. The analysis on the spatially distributed connections showed that this significance is strong only for within the single electrode region and in the front-to-back direction. The presented methodological framework is for the whole-night sleep recordings, but it also carries general implications for other global neural states.
Publisher:
Relation: Neuroscience
Identifier: oai:repository.ukim.mk:20.500.12188/26505
Identifier: http://hdl.handle.net/20.500.12188/26505Identifier: https://doi.org/10.1016/j.neuroscience.2023.05.016
